code: def read_from_tratz_2011(file_full_path, labeled_dict):
      """
      Read tratz_2011 files and print re-formatted csv files
  
      Args:
          file_full_path (str): file path
          labeled_dict (dict): dictionary with prepared labels
      """
      # 1. read the data
      with open(
          file_full_path, "r", encoding="utf-8-sig"
      ) as input_file:
          reader = csv.reader(
              (line.replace("\0", "") for line in input_file)
          )
          reader_list = list(reader)
          csv_data = []
          for index, row in enumerate(reader_list):
              if index in labeled_dict[False]:
                  csv_data.append(rebuild_row(row, False))
              if index in labeled_dict[True]:
                  csv_data.append(rebuild_row(row, True))
          # 2. write to csv file
          write_csv(csv_data, file_full_path)
  
file_path: nlp-architect/examples/np_semantic_segmentation/preprocess_tratz2011.py
imports: from __future__ import unicode_literals, print_function, division, absolute_import
  import argparse
  import csv
  import os
  from .data import absolute_path
  from nlp_architect.utils.io import validate_existing_directory
label_desc: Use image node_id 2 for calling a global function with example usage: read_from_tratz_2011(file_full_path, labeled_dict) without return types